Overview

Dataset statistics

Number of variables35
Number of observations88003
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.5 MiB
Average record size in memory280.0 B

Variable types

Numeric13
Text10
Categorical9
DateTime3

Alerts

address_id is highly overall correlated with customer_id and 1 other fieldsHigh correlation
amount is highly overall correlated with rental_rateHigh correlation
customer_id is highly overall correlated with address_id and 1 other fieldsHigh correlation
film_id is highly overall correlated with inventory_idHigh correlation
inventory_id is highly overall correlated with film_idHigh correlation
payment_id is highly overall correlated with address_id and 1 other fieldsHigh correlation
rental_rate is highly overall correlated with amountHigh correlation
staff_fn is highly overall correlated with staff_id and 2 other fieldsHigh correlation
staff_id is highly overall correlated with staff_fn and 2 other fieldsHigh correlation
staff_ln is highly overall correlated with staff_fn and 2 other fieldsHigh correlation
store_id is highly overall correlated with staff_fn and 2 other fieldsHigh correlation
active is highly imbalanced (82.7%)Imbalance

Reproduction

Analysis started2026-01-26 07:06:05.411937
Analysis finished2026-01-26 07:06:14.910463
Duration9.5 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

film_id
Real number (ℝ)

High correlation 

Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.01051
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:14.938000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1255
median496
Q3752
95-th percentile952
Maximum1000
Range999
Interquartile range (IQR)497

Descriptive statistics

Standard deviation288.36419
Coefficient of variation (CV)0.57441863
Kurtosis-1.1874154
Mean502.01051
Median Absolute Deviation (MAD)247
Skewness-0.010096326
Sum44178431
Variance83153.907
MonotonicityNot monotonic
2026-01-26T10:06:14.974866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489288
 
0.3%
892276
 
0.3%
880275
 
0.3%
249273
 
0.3%
649264
 
0.3%
301261
 
0.3%
764252
 
0.3%
369248
 
0.3%
418248
 
0.3%
966240
 
0.3%
Other values (945)85378
97.0%
ValueCountFrequency (%)
1230
0.3%
228
 
< 0.1%
360
 
0.1%
4115
0.1%
560
 
0.1%
6147
0.2%
775
 
0.1%
872
 
0.1%
9108
0.1%
10184
0.2%
ValueCountFrequency (%)
100093
0.1%
99985
0.1%
99854
 
0.1%
99730
 
< 0.1%
99635
 
< 0.1%
99523
 
< 0.1%
99478
0.1%
993180
0.2%
99256
 
0.1%
99164
 
0.1%

title
Text

Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:15.087380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length22
Mean length14.314387
Min length8

Characters and Unicode

Total characters1259709
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPATIENT SISTER
2nd rowPATIENT SISTER
3rd rowPATIENT SISTER
4th rowPATIENT SISTER
5th rowTALENTED HOMICIDE
ValueCountFrequency (%)
heartbreakers659
 
0.4%
boondock655
 
0.4%
armageddon647
 
0.4%
hellfighters642
 
0.4%
apollo625
 
0.4%
polish597
 
0.3%
shakespeare576
 
0.3%
desire568
 
0.3%
instinct558
 
0.3%
love548
 
0.3%
Other values (974)169931
96.5%
2026-01-26T10:06:15.233401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E124786
 
9.9%
A106224
 
8.4%
R91956
 
7.3%
O88848
 
7.1%
88003
 
7.0%
N83362
 
6.6%
I83028
 
6.6%
S78689
 
6.2%
T72889
 
5.8%
L60404
 
4.8%
Other values (17)381520
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1259709
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E124786
 
9.9%
A106224
 
8.4%
R91956
 
7.3%
O88848
 
7.1%
88003
 
7.0%
N83362
 
6.6%
I83028
 
6.6%
S78689
 
6.2%
T72889
 
5.8%
L60404
 
4.8%
Other values (17)381520
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1259709
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E124786
 
9.9%
A106224
 
8.4%
R91956
 
7.3%
O88848
 
7.1%
88003
 
7.0%
N83362
 
6.6%
I83028
 
6.6%
S78689
 
6.2%
T72889
 
5.8%
L60404
 
4.8%
Other values (17)381520
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1259709
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E124786
 
9.9%
A106224
 
8.4%
R91956
 
7.3%
O88848
 
7.1%
88003
 
7.0%
N83362
 
6.6%
I83028
 
6.6%
S78689
 
6.2%
T72889
 
5.8%
L60404
 
4.8%
Other values (17)381520
30.3%
Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:15.314091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length130
Median length115
Mean length94.30574
Min length70

Characters and Unicode

Total characters8299188
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia
2nd rowA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia
3rd rowA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia
4th rowA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia
5th rowA Lacklusture Panorama of a Dentist And a Forensic Psychologist who must Outrace a Pioneer in A U-Boat
ValueCountFrequency (%)
a388644
24.0%
of91053
 
5.6%
and88003
 
5.4%
who88003
 
5.4%
must88003
 
5.4%
in88003
 
5.4%
the16917
 
1.0%
mad15834
 
1.0%
shark10686
 
0.7%
boat10387
 
0.6%
Other values (139)731780
45.2%
2026-01-26T10:06:15.435433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1529310
18.4%
a727487
 
8.8%
n572021
 
6.9%
o508454
 
6.1%
e496140
 
6.0%
t473395
 
5.7%
i421059
 
5.1%
r339854
 
4.1%
s298869
 
3.6%
A286081
 
3.4%
Other values (42)2646518
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)8299188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1529310
18.4%
a727487
 
8.8%
n572021
 
6.9%
o508454
 
6.1%
e496140
 
6.0%
t473395
 
5.7%
i421059
 
5.1%
r339854
 
4.1%
s298869
 
3.6%
A286081
 
3.4%
Other values (42)2646518
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8299188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1529310
18.4%
a727487
 
8.8%
n572021
 
6.9%
o508454
 
6.1%
e496140
 
6.0%
t473395
 
5.7%
i421059
 
5.1%
r339854
 
4.1%
s298869
 
3.6%
A286081
 
3.4%
Other values (42)2646518
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8299188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1529310
18.4%
a727487
 
8.8%
n572021
 
6.9%
o508454
 
6.1%
e496140
 
6.0%
t473395
 
5.7%
i421059
 
5.1%
r339854
 
4.1%
s298869
 
3.6%
A286081
 
3.4%
Other values (42)2646518
31.9%

rental_duration
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
6
19279 
4
18643 
3
18215 
5
17551 
7
14315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters88003
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row6

Common Values

ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318215
20.7%
517551
19.9%
714315
16.3%

Length

2026-01-26T10:06:15.470559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:15.499904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318215
20.7%
517551
19.9%
714315
16.3%

Most occurring characters

ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318215
20.7%
517551
19.9%
714315
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318215
20.7%
517551
19.9%
714315
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318215
20.7%
517551
19.9%
714315
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318215
20.7%
517551
19.9%
714315
16.3%

rental_rate
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
0.99
31137 
2.99
28959 
4.99
27907 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters352012
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.99
2nd row0.99
3rd row0.99
4th row0.99
5th row0.99

Common Values

ValueCountFrequency (%)
0.9931137
35.4%
2.9928959
32.9%
4.9927907
31.7%

Length

2026-01-26T10:06:15.531548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:15.551855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.9931137
35.4%
2.9928959
32.9%
4.9927907
31.7%

Most occurring characters

ValueCountFrequency (%)
9176006
50.0%
.88003
25.0%
031137
 
8.8%
228959
 
8.2%
427907
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)352012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9176006
50.0%
.88003
25.0%
031137
 
8.8%
228959
 
8.2%
427907
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)352012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9176006
50.0%
.88003
25.0%
031137
 
8.8%
228959
 
8.2%
427907
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)352012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9176006
50.0%
.88003
25.0%
031137
 
8.8%
228959
 
8.2%
427907
 
7.9%

length
Real number (ℝ)

Distinct140
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.10804
Minimum46
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:15.579990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile52
Q180
median114
Q3150
95-th percentile178
Maximum185
Range139
Interquartile range (IQR)70

Descriptive statistics

Standard deviation40.497718
Coefficient of variation (CV)0.35182354
Kurtosis-1.1972212
Mean115.10804
Median Absolute Deviation (MAD)35
Skewness0.027207201
Sum10129853
Variance1640.0652
MonotonicityNot monotonic
2026-01-26T10:06:15.617534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851495
 
1.7%
1121358
 
1.5%
1781355
 
1.5%
841178
 
1.3%
801072
 
1.2%
631070
 
1.2%
751056
 
1.2%
921041
 
1.2%
1791019
 
1.2%
1611008
 
1.1%
Other values (130)76351
86.8%
ValueCountFrequency (%)
46585
0.7%
47732
0.8%
48832
0.9%
49369
0.4%
50563
0.6%
51647
0.7%
52704
0.8%
53759
0.9%
54524
0.6%
55336
0.4%
ValueCountFrequency (%)
185936
1.1%
184393
 
0.4%
183417
 
0.5%
182252
 
0.3%
181837
1.0%
180376
 
0.4%
1791019
1.2%
1781355
1.5%
177660
0.7%
176893
1.0%

replacement_cost
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.190914
Minimum9.99
Maximum29.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:15.648721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.99
5-th percentile10.99
Q114.99
median20.99
Q324.99
95-th percentile29.99
Maximum29.99
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0875717
Coefficient of variation (CV)0.30150056
Kurtosis-1.2236016
Mean20.190914
Median Absolute Deviation (MAD)5
Skewness-0.035295321
Sum1776861
Variance37.058529
MonotonicityNot monotonic
2026-01-26T10:06:15.678358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22.995626
 
6.4%
27.995215
 
5.9%
12.995024
 
5.7%
21.994902
 
5.6%
14.994893
 
5.6%
29.994686
 
5.3%
20.994672
 
5.3%
26.994264
 
4.8%
13.994213
 
4.8%
11.994167
 
4.7%
Other values (11)40341
45.8%
ValueCountFrequency (%)
9.993924
4.5%
10.993526
4.0%
11.994167
4.7%
12.995024
5.7%
13.994213
4.8%
14.994893
5.6%
15.993141
3.6%
16.993593
4.1%
17.993755
4.3%
18.993744
4.3%
ValueCountFrequency (%)
29.994686
5.3%
28.994080
4.6%
27.995215
5.9%
26.994264
4.8%
25.993727
4.2%
24.993431
3.9%
23.993870
4.4%
22.995626
6.4%
21.994902
5.6%
20.994672
5.3%

rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
PG-13
19065 
PG
18635 
NC-17
17630 
R
17210 
G
15463 

Length

Max length5
Median length2
Mean length2.8796518
Min length1

Characters and Unicode

Total characters253418
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC-17
2nd rowNC-17
3rd rowNC-17
4th rowNC-17
5th rowPG

Common Values

ValueCountFrequency (%)
PG-1319065
21.7%
PG18635
21.2%
NC-1717630
20.0%
R17210
19.6%
G15463
17.6%

Length

2026-01-26T10:06:15.711745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:15.736472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pg-1319065
21.7%
pg18635
21.2%
nc-1717630
20.0%
r17210
19.6%
g15463
17.6%

Most occurring characters

ValueCountFrequency (%)
G53163
21.0%
P37700
14.9%
-36695
14.5%
136695
14.5%
319065
 
7.5%
N17630
 
7.0%
C17630
 
7.0%
717630
 
7.0%
R17210
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)253418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G53163
21.0%
P37700
14.9%
-36695
14.5%
136695
14.5%
319065
 
7.5%
N17630
 
7.0%
C17630
 
7.0%
717630
 
7.0%
R17210
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)253418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G53163
21.0%
P37700
14.9%
-36695
14.5%
136695
14.5%
319065
 
7.5%
N17630
 
7.0%
C17630
 
7.0%
717630
 
7.0%
R17210
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)253418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G53163
21.0%
P37700
14.9%
-36695
14.5%
136695
14.5%
319065
 
7.5%
N17630
 
7.0%
C17630
 
7.0%
717630
 
7.0%
R17210
 
6.8%

actor_id
Real number (ℝ)

Distinct200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.96492
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:15.769474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q152
median102
Q3149
95-th percentile191
Maximum200
Range199
Interquartile range (IQR)97

Descriptive statistics

Standard deviation56.908857
Coefficient of variation (CV)0.56364979
Kurtosis-1.1749861
Mean100.96492
Median Absolute Deviation (MAD)48
Skewness0.0019690918
Sum8885216
Variance3238.618
MonotonicityNot monotonic
2026-01-26T10:06:15.807061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107753
 
0.9%
181678
 
0.8%
198675
 
0.8%
144654
 
0.7%
102640
 
0.7%
60612
 
0.7%
150611
 
0.7%
37605
 
0.7%
23604
 
0.7%
90599
 
0.7%
Other values (190)81572
92.7%
ValueCountFrequency (%)
1305
0.3%
2387
0.4%
3311
0.4%
4274
0.3%
5496
0.6%
6279
0.3%
7479
0.5%
8317
0.4%
9381
0.4%
10362
0.4%
ValueCountFrequency (%)
200350
0.4%
199255
 
0.3%
198675
0.8%
197553
0.6%
196450
0.5%
195450
0.5%
194380
0.4%
193449
0.5%
192458
0.5%
191523
0.6%
Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:15.910883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length8
Mean length5.3009897
Min length2

Characters and Unicode

Total characters466503
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEAN
2nd rowMILLA
3rd rowVAL
4th rowLUCILLE
5th rowJADA
ValueCountFrequency (%)
penelope1661
 
1.9%
kenneth1643
 
1.9%
jayne1565
 
1.8%
matthew1518
 
1.7%
julia1435
 
1.6%
groucho1350
 
1.5%
morgan1340
 
1.5%
ed1311
 
1.5%
burt1309
 
1.5%
christian1286
 
1.5%
Other values (118)73585
83.6%
2026-01-26T10:06:16.041885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E65227
14.0%
A52745
 
11.3%
R41096
 
8.8%
N40755
 
8.7%
L29594
 
6.3%
I29427
 
6.3%
O18598
 
4.0%
S18575
 
4.0%
T18157
 
3.9%
M17209
 
3.7%
Other values (14)135120
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)466503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E65227
14.0%
A52745
 
11.3%
R41096
 
8.8%
N40755
 
8.7%
L29594
 
6.3%
I29427
 
6.3%
O18598
 
4.0%
S18575
 
4.0%
T18157
 
3.9%
M17209
 
3.7%
Other values (14)135120
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)466503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E65227
14.0%
A52745
 
11.3%
R41096
 
8.8%
N40755
 
8.7%
L29594
 
6.3%
I29427
 
6.3%
O18598
 
4.0%
S18575
 
4.0%
T18157
 
3.9%
M17209
 
3.7%
Other values (14)135120
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)466503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E65227
14.0%
A52745
 
11.3%
R41096
 
8.8%
N40755
 
8.7%
L29594
 
6.3%
I29427
 
6.3%
O18598
 
4.0%
S18575
 
4.0%
T18157
 
3.9%
M17209
 
3.7%
Other values (14)135120
29.0%
Distinct121
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:16.145700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.2453439
Min length3

Characters and Unicode

Total characters549609
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGUINESS
2nd rowKEITEL
3rd rowBOLGER
4th rowTRACY
5th rowRYDER
ValueCountFrequency (%)
kilmer2145
 
2.4%
nolte2119
 
2.4%
temple1788
 
2.0%
degeneres1611
 
1.8%
keitel1587
 
1.8%
berry1480
 
1.7%
torn1478
 
1.7%
hoffman1455
 
1.7%
guiness1426
 
1.6%
garland1394
 
1.6%
Other values (111)71520
81.3%
2026-01-26T10:06:16.278170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E68637
12.5%
N44882
 
8.2%
R41122
 
7.5%
A39731
 
7.2%
O39696
 
7.2%
L39456
 
7.2%
I33956
 
6.2%
S32309
 
5.9%
T23679
 
4.3%
D21504
 
3.9%
Other values (17)164637
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)549609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E68637
12.5%
N44882
 
8.2%
R41122
 
7.5%
A39731
 
7.2%
O39696
 
7.2%
L39456
 
7.2%
I33956
 
6.2%
S32309
 
5.9%
T23679
 
4.3%
D21504
 
3.9%
Other values (17)164637
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)549609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E68637
12.5%
N44882
 
8.2%
R41122
 
7.5%
A39731
 
7.2%
O39696
 
7.2%
L39456
 
7.2%
I33956
 
6.2%
S32309
 
5.9%
T23679
 
4.3%
D21504
 
3.9%
Other values (17)164637
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)549609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E68637
12.5%
N44882
 
8.2%
R41122
 
7.5%
A39731
 
7.2%
O39696
 
7.2%
L39456
 
7.2%
I33956
 
6.2%
S32309
 
5.9%
T23679
 
4.3%
D21504
 
3.9%
Other values (17)164637
30.0%

customer_id
Real number (ℝ)

High correlation 

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.17848
Minimum1
Maximum599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:16.313823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1148
median296
Q3446
95-th percentile568
Maximum599
Range598
Interquartile range (IQR)298

Descriptive statistics

Standard deviation172.26314
Coefficient of variation (CV)0.57966224
Kurtosis-1.1884762
Mean297.17848
Median Absolute Deviation (MAD)149
Skewness0.0088665271
Sum26152598
Variance29674.591
MonotonicityNot monotonic
2026-01-26T10:06:16.350392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148272
 
0.3%
526251
 
0.3%
197244
 
0.3%
144239
 
0.3%
75233
 
0.3%
236233
 
0.3%
29223
 
0.3%
257221
 
0.3%
178220
 
0.2%
410216
 
0.2%
Other values (589)85651
97.3%
ValueCountFrequency (%)
1156
0.2%
2175
0.2%
3144
0.2%
4121
0.1%
5183
0.2%
6171
0.2%
7191
0.2%
8152
0.2%
9129
0.1%
10123
0.1%
ValueCountFrequency (%)
599102
0.1%
598102
0.1%
597144
0.2%
596158
0.2%
595141
0.2%
594141
0.2%
593129
0.1%
592145
0.2%
591120
0.1%
590134
0.2%

cust_fn
Text

Distinct591
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:16.446006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.6736589
Min length2

Characters and Unicode

Total characters499299
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMARY
2nd rowMARY
3rd rowMARY
4th rowMARY
5th rowMARY
ValueCountFrequency (%)
marion388
 
0.4%
leslie325
 
0.4%
tracy303
 
0.3%
willie291
 
0.3%
terry291
 
0.3%
jessie290
 
0.3%
jamie277
 
0.3%
kelly272
 
0.3%
eleanor272
 
0.3%
karl251
 
0.3%
Other values (581)85043
96.6%
2026-01-26T10:06:16.569577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E62900
12.6%
A61300
12.3%
R47061
 
9.4%
N42781
 
8.6%
I38757
 
7.8%
L36735
 
7.4%
O23432
 
4.7%
T21523
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126309
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)499299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E62900
12.6%
A61300
12.3%
R47061
 
9.4%
N42781
 
8.6%
I38757
 
7.8%
L36735
 
7.4%
O23432
 
4.7%
T21523
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126309
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)499299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E62900
12.6%
A61300
12.3%
R47061
 
9.4%
N42781
 
8.6%
I38757
 
7.8%
L36735
 
7.4%
O23432
 
4.7%
T21523
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126309
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)499299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E62900
12.6%
A61300
12.3%
R47061
 
9.4%
N42781
 
8.6%
I38757
 
7.8%
L36735
 
7.4%
O23432
 
4.7%
T21523
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126309
25.3%

cust_ln
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:16.678982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.1921753
Min length2

Characters and Unicode

Total characters544930
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSMITH
2nd rowSMITH
3rd rowSMITH
4th rowSMITH
5th rowSMITH
ValueCountFrequency (%)
hunt272
 
0.3%
seal251
 
0.3%
peters244
 
0.3%
shaw239
 
0.3%
sanders233
 
0.3%
dean233
 
0.3%
hernandez223
 
0.3%
douglas221
 
0.3%
snyder220
 
0.2%
irby216
 
0.2%
Other values (589)85651
97.3%
2026-01-26T10:06:16.872958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E58281
 
10.7%
R54396
 
10.0%
A47366
 
8.7%
N43550
 
8.0%
O38964
 
7.2%
L38581
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26024
 
4.8%
H20092
 
3.7%
Other values (16)154843
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)544930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E58281
 
10.7%
R54396
 
10.0%
A47366
 
8.7%
N43550
 
8.0%
O38964
 
7.2%
L38581
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26024
 
4.8%
H20092
 
3.7%
Other values (16)154843
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)544930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E58281
 
10.7%
R54396
 
10.0%
A47366
 
8.7%
N43550
 
8.0%
O38964
 
7.2%
L38581
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26024
 
4.8%
H20092
 
3.7%
Other values (16)154843
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)544930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E58281
 
10.7%
R54396
 
10.0%
A47366
 
8.7%
N43550
 
8.0%
O38964
 
7.2%
L38581
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26024
 
4.8%
H20092
 
3.7%
Other values (16)154843
28.4%

email
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:16.971260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length38
Mean length31.865834
Min length26

Characters and Unicode

Total characters2804289
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMARY.SMITH@sakilacustomer.org
2nd rowMARY.SMITH@sakilacustomer.org
3rd rowMARY.SMITH@sakilacustomer.org
4th rowMARY.SMITH@sakilacustomer.org
5th rowMARY.SMITH@sakilacustomer.org
ValueCountFrequency (%)
eleanor.hunt@sakilacustomer.org272
 
0.3%
karl.seal@sakilacustomer.org251
 
0.3%
sue.peters@sakilacustomer.org244
 
0.3%
clara.shaw@sakilacustomer.org239
 
0.3%
tammy.sanders@sakilacustomer.org233
 
0.3%
marcia.dean@sakilacustomer.org233
 
0.3%
angela.hernandez@sakilacustomer.org223
 
0.3%
marsha.douglas@sakilacustomer.org221
 
0.3%
marion.snyder@sakilacustomer.org220
 
0.2%
curtis.irby@sakilacustomer.org216
 
0.2%
Other values (589)85651
97.3%
2026-01-26T10:06:17.105504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r176006
 
6.3%
.176006
 
6.3%
o176006
 
6.3%
s176006
 
6.3%
a176006
 
6.3%
E121181
 
4.3%
A108666
 
3.9%
R101457
 
3.6%
l88003
 
3.1%
g88003
 
3.1%
Other values (31)1416949
50.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2804289
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r176006
 
6.3%
.176006
 
6.3%
o176006
 
6.3%
s176006
 
6.3%
a176006
 
6.3%
E121181
 
4.3%
A108666
 
3.9%
R101457
 
3.6%
l88003
 
3.1%
g88003
 
3.1%
Other values (31)1416949
50.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2804289
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r176006
 
6.3%
.176006
 
6.3%
o176006
 
6.3%
s176006
 
6.3%
a176006
 
6.3%
E121181
 
4.3%
A108666
 
3.9%
R101457
 
3.6%
l88003
 
3.1%
g88003
 
3.1%
Other values (31)1416949
50.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2804289
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r176006
 
6.3%
.176006
 
6.3%
o176006
 
6.3%
s176006
 
6.3%
a176006
 
6.3%
E121181
 
4.3%
A108666
 
3.9%
R101457
 
3.6%
l88003
 
3.1%
g88003
 
3.1%
Other values (31)1416949
50.5%

active
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
1
85737 
0
 
2266

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters88003
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
185737
97.4%
02266
 
2.6%

Length

2026-01-26T10:06:17.139372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:17.159449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
185737
97.4%
02266
 
2.6%

Most occurring characters

ValueCountFrequency (%)
185737
97.4%
02266
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
185737
97.4%
02266
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
185737
97.4%
02266
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
185737
97.4%
02266
 
2.6%

rental_id
Real number (ℝ)

Distinct16004
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8032.5002
Minimum1
Maximum16049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:17.183708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile816.1
Q14026.5
median8039
Q312027
95-th percentile15247
Maximum16049
Range16048
Interquartile range (IQR)8000.5

Descriptive statistics

Standard deviation4631.7329
Coefficient of variation (CV)0.57662407
Kurtosis-1.2006239
Mean8032.5002
Median Absolute Deviation (MAD)4002
Skewness-0.0011786957
Sum7.0688411 × 108
Variance21452950
MonotonicityNot monotonic
2026-01-26T10:06:17.223292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120
 
< 0.1%
1420015
 
< 0.1%
484515
 
< 0.1%
744615
 
< 0.1%
801415
 
< 0.1%
1270115
 
< 0.1%
715215
 
< 0.1%
593015
 
< 0.1%
1518715
 
< 0.1%
279615
 
< 0.1%
Other values (15994)87848
99.8%
ValueCountFrequency (%)
120
< 0.1%
25
 
< 0.1%
32
 
< 0.1%
44
 
< 0.1%
58
 
< 0.1%
64
 
< 0.1%
76
 
< 0.1%
84
 
< 0.1%
93
 
< 0.1%
107
 
< 0.1%
ValueCountFrequency (%)
160496
< 0.1%
160482
 
< 0.1%
160477
< 0.1%
160465
< 0.1%
160455
< 0.1%
160445
< 0.1%
160437
< 0.1%
160423
 
< 0.1%
160416
< 0.1%
160408
< 0.1%
Distinct15776
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
Minimum2005-05-24 22:53:30
Maximum2006-02-14 15:16:03
Invalid dates0
Invalid dates (%)0.0%
2026-01-26T10:06:17.262060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:17.300712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct15796
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
Minimum2005-05-25 23:55:21
Maximum2005-09-02 02:35:22
Invalid dates0
Invalid dates (%)0.0%
2026-01-26T10:06:17.337718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:17.379123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

payment_id
Real number (ℝ)

High correlation 

Distinct16009
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8026.4141
Minimum1
Maximum16049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:17.416622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile795.1
Q14037.5
median8034
Q312029
95-th percentile15218
Maximum16049
Range16048
Interquartile range (IQR)7991.5

Descriptive statistics

Standard deviation4627.9028
Coefficient of variation (CV)0.57658411
Kurtosis-1.1977652
Mean8026.4141
Median Absolute Deviation (MAD)3996
Skewness-0.0065076363
Sum7.0634852 × 108
Variance21417485
MonotonicityIncreasing
2026-01-26T10:06:17.455035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1312915
 
< 0.1%
566215
 
< 0.1%
1246315
 
< 0.1%
228015
 
< 0.1%
685315
 
< 0.1%
1000415
 
< 0.1%
1320215
 
< 0.1%
1483915
 
< 0.1%
945715
 
< 0.1%
28815
 
< 0.1%
Other values (15999)87853
99.8%
ValueCountFrequency (%)
14
< 0.1%
25
< 0.1%
31
 
< 0.1%
43
< 0.1%
51
 
< 0.1%
66
< 0.1%
74
< 0.1%
86
< 0.1%
95
< 0.1%
105
< 0.1%
ValueCountFrequency (%)
160494
< 0.1%
160488
< 0.1%
160475
< 0.1%
160464
< 0.1%
160453
 
< 0.1%
160445
< 0.1%
160433
 
< 0.1%
160425
< 0.1%
160419
< 0.1%
160406
< 0.1%

amount
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1771512
Minimum0
Maximum11.99
Zeros133
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:17.486397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q12.99
median3.99
Q34.99
95-th percentile8.99
Maximum11.99
Range11.99
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3456479
Coefficient of variation (CV)0.56154248
Kurtosis-0.24037839
Mean4.1771512
Median Absolute Deviation (MAD)1
Skewness0.46837152
Sum367601.84
Variance5.502064
MonotonicityNot monotonic
2026-01-26T10:06:17.513364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4.9920434
23.2%
2.9919873
22.6%
0.9916275
18.5%
5.997181
 
8.2%
3.996354
 
7.2%
6.995929
 
6.7%
7.993654
 
4.2%
1.993498
 
4.0%
8.992633
 
3.0%
9.991302
 
1.5%
Other values (9)870
 
1.0%
ValueCountFrequency (%)
0133
 
0.2%
0.9916275
18.5%
1.984
 
< 0.1%
1.993498
 
4.0%
2.9919873
22.6%
3.9843
 
< 0.1%
3.996354
 
7.2%
4.9920434
23.2%
5.9847
 
0.1%
5.997181
 
8.2%
ValueCountFrequency (%)
11.9949
 
0.1%
10.99549
 
0.6%
9.991302
 
1.5%
9.986
 
< 0.1%
8.992633
 
3.0%
8.977
 
< 0.1%
7.993654
4.2%
7.9832
 
< 0.1%
6.995929
6.7%
5.997181
8.2%
Distinct15780
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
Minimum2005-05-24 22:53:30
Maximum2006-02-14 15:16:03
Invalid dates0
Invalid dates (%)0.0%
2026-01-26T10:06:17.545824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:17.584482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

inventory_id
Real number (ℝ)

High correlation 

Distinct4567
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2296.0328
Minimum1
Maximum4581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:17.620398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile214
Q11158
median2287
Q33428
95-th percentile4367
Maximum4581
Range4580
Interquartile range (IQR)2270

Descriptive statistics

Standard deviation1321.4595
Coefficient of variation (CV)0.57554035
Kurtosis-1.1822263
Mean2296.0328
Median Absolute Deviation (MAD)1134
Skewness-0.0059873723
Sum2.0205778 × 108
Variance1746255.3
MonotonicityNot monotonic
2026-01-26T10:06:17.655057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112265
 
0.1%
112365
 
0.1%
242360
 
0.1%
189960
 
0.1%
15860
 
0.1%
233760
 
0.1%
409560
 
0.1%
237160
 
0.1%
242460
 
0.1%
332855
 
0.1%
Other values (4557)87398
99.3%
ValueCountFrequency (%)
130
< 0.1%
250
0.1%
320
 
< 0.1%
420
 
< 0.1%
650
0.1%
740
< 0.1%
820
 
< 0.1%
98
 
< 0.1%
1012
 
< 0.1%
118
 
< 0.1%
ValueCountFrequency (%)
458115
< 0.1%
45806
 
< 0.1%
457915
< 0.1%
45789
 
< 0.1%
457715
< 0.1%
457612
< 0.1%
457512
< 0.1%
45749
 
< 0.1%
457325
< 0.1%
457220
< 0.1%

store_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
1
47969 
2
40034 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters88003
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Length

2026-01-26T10:06:17.688335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:17.707806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring characters

ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

staff_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
1
47969 
2
40034 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters88003
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Length

2026-01-26T10:06:17.732827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:17.803783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring characters

ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)88003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
147969
54.5%
240034
45.5%

staff_fn
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
Mike
47969 
Jon
40034 

Length

Max length4
Median length4
Mean length3.5450837
Min length3

Characters and Unicode

Total characters311978
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMike
2nd rowMike
3rd rowMike
4th rowMike
5th rowMike

Common Values

ValueCountFrequency (%)
Mike47969
54.5%
Jon40034
45.5%

Length

2026-01-26T10:06:17.826402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:17.844318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mike47969
54.5%
jon40034
45.5%

Most occurring characters

ValueCountFrequency (%)
M47969
15.4%
i47969
15.4%
k47969
15.4%
e47969
15.4%
J40034
12.8%
o40034
12.8%
n40034
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)311978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M47969
15.4%
i47969
15.4%
k47969
15.4%
e47969
15.4%
J40034
12.8%
o40034
12.8%
n40034
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)311978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M47969
15.4%
i47969
15.4%
k47969
15.4%
e47969
15.4%
J40034
12.8%
o40034
12.8%
n40034
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)311978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M47969
15.4%
i47969
15.4%
k47969
15.4%
e47969
15.4%
J40034
12.8%
o40034
12.8%
n40034
12.8%

staff_ln
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
Hillyer
47969 
Stephens
40034 

Length

Max length8
Median length7
Mean length7.4549163
Min length7

Characters and Unicode

Total characters656055
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHillyer
2nd rowHillyer
3rd rowHillyer
4th rowHillyer
5th rowHillyer

Common Values

ValueCountFrequency (%)
Hillyer47969
54.5%
Stephens40034
45.5%

Length

2026-01-26T10:06:17.867644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-26T10:06:17.885617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hillyer47969
54.5%
stephens40034
45.5%

Most occurring characters

ValueCountFrequency (%)
e128037
19.5%
l95938
14.6%
H47969
 
7.3%
i47969
 
7.3%
y47969
 
7.3%
r47969
 
7.3%
S40034
 
6.1%
t40034
 
6.1%
p40034
 
6.1%
h40034
 
6.1%
Other values (2)80068
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)656055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e128037
19.5%
l95938
14.6%
H47969
 
7.3%
i47969
 
7.3%
y47969
 
7.3%
r47969
 
7.3%
S40034
 
6.1%
t40034
 
6.1%
p40034
 
6.1%
h40034
 
6.1%
Other values (2)80068
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)656055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e128037
19.5%
l95938
14.6%
H47969
 
7.3%
i47969
 
7.3%
y47969
 
7.3%
r47969
 
7.3%
S40034
 
6.1%
t40034
 
6.1%
p40034
 
6.1%
h40034
 
6.1%
Other values (2)80068
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)656055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e128037
19.5%
l95938
14.6%
H47969
 
7.3%
i47969
 
7.3%
y47969
 
7.3%
r47969
 
7.3%
S40034
 
6.1%
t40034
 
6.1%
p40034
 
6.1%
h40034
 
6.1%
Other values (2)80068
12.2%

address_id
Real number (ℝ)

High correlation 

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.89364
Minimum5
Maximum605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:17.914854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile33
Q1152
median301
Q3451
95-th percentile574
Maximum605
Range600
Interquartile range (IQR)299

Descriptive statistics

Standard deviation172.89386
Coefficient of variation (CV)0.57269791
Kurtosis-1.188725
Mean301.89364
Median Absolute Deviation (MAD)149
Skewness0.010566113
Sum26567546
Variance29892.286
MonotonicityNot monotonic
2026-01-26T10:06:17.952572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152272
 
0.3%
532251
 
0.3%
201244
 
0.3%
148239
 
0.3%
79233
 
0.3%
240233
 
0.3%
33223
 
0.3%
262221
 
0.3%
182220
 
0.2%
415216
 
0.2%
Other values (589)85651
97.3%
ValueCountFrequency (%)
5156
0.2%
6175
0.2%
7144
0.2%
8121
0.1%
9183
0.2%
10171
0.2%
11191
0.2%
12152
0.2%
13129
0.1%
14123
0.1%
ValueCountFrequency (%)
605102
0.1%
604102
0.1%
603144
0.2%
602158
0.2%
601141
0.2%
600141
0.2%
599129
0.1%
598145
0.2%
597120
0.1%
596134
0.2%

address
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:18.061650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length34
Mean length19.461007
Min length9

Characters and Unicode

Total characters1712627
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1913 Hanoi Way
2nd row1913 Hanoi Way
3rd row1913 Hanoi Way
4th row1913 Hanoi Way
5th row1913 Hanoi Way
ValueCountFrequency (%)
parkway11137
 
3.9%
manor9908
 
3.4%
avenue8963
 
3.1%
way8854
 
3.1%
lane8768
 
3.0%
street8622
 
3.0%
place8225
 
2.8%
loop8071
 
2.8%
boulevard7812
 
2.7%
drive7643
 
2.6%
Other values (957)201269
69.6%
2026-01-26T10:06:18.205830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
201269
 
11.8%
a179026
 
10.5%
e110215
 
6.4%
o87025
 
5.1%
r83511
 
4.9%
n82203
 
4.8%
167763
 
4.0%
i51598
 
3.0%
l51231
 
3.0%
u49057
 
2.9%
Other values (56)749729
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1712627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
201269
 
11.8%
a179026
 
10.5%
e110215
 
6.4%
o87025
 
5.1%
r83511
 
4.9%
n82203
 
4.8%
167763
 
4.0%
i51598
 
3.0%
l51231
 
3.0%
u49057
 
2.9%
Other values (56)749729
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1712627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
201269
 
11.8%
a179026
 
10.5%
e110215
 
6.4%
o87025
 
5.1%
r83511
 
4.9%
n82203
 
4.8%
167763
 
4.0%
i51598
 
3.0%
l51231
 
3.0%
u49057
 
2.9%
Other values (56)749729
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1712627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
201269
 
11.8%
a179026
 
10.5%
e110215
 
6.4%
o87025
 
5.1%
r83511
 
4.9%
n82203
 
4.8%
167763
 
4.0%
i51598
 
3.0%
l51231
 
3.0%
u49057
 
2.9%
Other values (56)749729
43.8%

postal_code
Real number (ℝ)

Distinct596
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50338.395
Minimum3
Maximum99865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:18.243463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4855
Q125220
median50805
Q374750
95-th percentile95509
Maximum99865
Range99862
Interquartile range (IQR)49530

Descriptive statistics

Standard deviation28837.245
Coefficient of variation (CV)0.5728678
Kurtosis-1.1638411
Mean50338.395
Median Absolute Deviation (MAD)24669
Skewness-0.019266617
Sum4.4299297 × 109
Variance8.3158673 × 108
MonotonicityNot monotonic
2026-01-26T10:06:18.282112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52137299
 
0.3%
9668284
 
0.3%
92150272
 
0.3%
22474270
 
0.3%
31342251
 
0.3%
89459244
 
0.3%
30861239
 
0.3%
72394233
 
0.3%
18727233
 
0.3%
65750223
 
0.3%
Other values (586)85455
97.1%
ValueCountFrequency (%)
3122
0.1%
400138
0.2%
504130
0.1%
841108
0.1%
943133
0.2%
966118
0.1%
1027132
0.1%
1079122
0.1%
1195146
0.2%
1545190
0.2%
ValueCountFrequency (%)
99865126
0.1%
99780165
0.2%
99552156
0.2%
99457168
0.2%
99405178
0.2%
99124124
0.1%
98889141
0.2%
98883164
0.2%
98775121
0.1%
98573157
0.2%

city_id
Real number (ℝ)

Distinct597
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.38698
Minimum1
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:18.318125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q1148
median304
Q3452
95-th percentile569
Maximum600
Range599
Interquartile range (IQR)304

Descriptive statistics

Standard deviation173.50356
Coefficient of variation (CV)0.57568368
Kurtosis-1.2104514
Mean301.38698
Median Absolute Deviation (MAD)152
Skewness-0.016064084
Sum26522958
Variance30103.486
MonotonicityNot monotonic
2026-01-26T10:06:18.354146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42275
 
0.3%
442272
 
0.3%
312271
 
0.3%
101251
 
0.3%
109244
 
0.3%
340239
 
0.3%
108233
 
0.3%
527233
 
0.3%
474223
 
0.3%
64221
 
0.3%
Other values (587)85541
97.2%
ValueCountFrequency (%)
1156
0.2%
2124
0.1%
3177
0.2%
4160
0.2%
5131
0.1%
6123
0.1%
7178
0.2%
8136
0.2%
9164
0.2%
10157
0.2%
ValueCountFrequency (%)
600131
0.1%
599171
0.2%
598157
0.2%
597138
0.2%
596153
0.2%
59581
0.1%
594123
0.1%
593144
0.2%
592139
0.2%
591137
0.2%

city
Text

Distinct597
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:18.438851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length21
Mean length8.3923616
Min length2

Characters and Unicode

Total characters738553
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSasebo
2nd rowSasebo
3rd rowSasebo
4th rowSasebo
5th rowSasebo
ValueCountFrequency (%)
de1936
 
1.8%
san946
 
0.9%
la727
 
0.7%
del581
 
0.5%
santa578
 
0.5%
el433
 
0.4%
hill411
 
0.4%
santiago395
 
0.4%
plata353
 
0.3%
felipe344
 
0.3%
Other values (673)100711
93.8%
2026-01-26T10:06:18.557918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a109633
 
14.8%
n52078
 
7.1%
o47635
 
6.4%
i46556
 
6.3%
e42704
 
5.8%
r38393
 
5.2%
u34239
 
4.6%
l30362
 
4.1%
s24885
 
3.4%
t24653
 
3.3%
Other values (47)287415
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)738553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a109633
 
14.8%
n52078
 
7.1%
o47635
 
6.4%
i46556
 
6.3%
e42704
 
5.8%
r38393
 
5.2%
u34239
 
4.6%
l30362
 
4.1%
s24885
 
3.4%
t24653
 
3.3%
Other values (47)287415
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)738553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a109633
 
14.8%
n52078
 
7.1%
o47635
 
6.4%
i46556
 
6.3%
e42704
 
5.8%
r38393
 
5.2%
u34239
 
4.6%
l30362
 
4.1%
s24885
 
3.4%
t24653
 
3.3%
Other values (47)287415
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)738553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a109633
 
14.8%
n52078
 
7.1%
o47635
 
6.4%
i46556
 
6.3%
e42704
 
5.8%
r38393
 
5.2%
u34239
 
4.6%
l30362
 
4.1%
s24885
 
3.4%
t24653
 
3.3%
Other values (47)287415
38.9%

country_id
Real number (ℝ)

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.85813
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:18.593938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q129
median50
Q380
95-th percentile103
Maximum109
Range108
Interquartile range (IQR)51

Descriptive statistics

Standard deviation30.066213
Coefficient of variation (CV)0.52879356
Kurtosis-1.160329
Mean56.85813
Median Absolute Deviation (MAD)27
Skewness0.056191588
Sum5003686
Variance903.97716
MonotonicityNot monotonic
2026-01-26T10:06:18.630455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448637
 
9.8%
237724
 
8.8%
1035428
 
6.2%
504565
 
5.2%
604493
 
5.1%
154058
 
4.6%
803895
 
4.4%
753160
 
3.6%
972094
 
2.4%
452014
 
2.3%
Other values (98)41935
47.7%
ValueCountFrequency (%)
1109
 
0.1%
2457
 
0.5%
3111
 
0.1%
4301
 
0.3%
5195
 
0.2%
61884
2.1%
7150
 
0.2%
9403
 
0.5%
10302
 
0.3%
11134
 
0.2%
ValueCountFrequency (%)
109174
 
0.2%
108328
 
0.4%
107628
 
0.7%
106190
 
0.2%
105908
 
1.0%
104963
 
1.1%
1035428
6.2%
1021210
 
1.4%
101476
 
0.5%
100823
 
0.9%

country
Text

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
2026-01-26T10:06:18.733562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length29
Mean length8.1360181
Min length4

Characters and Unicode

Total characters715994
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJapan
2nd rowJapan
3rd rowJapan
4th rowJapan
5th rowJapan
ValueCountFrequency (%)
india8637
 
8.0%
china7724
 
7.2%
united7114
 
6.6%
states5428
 
5.0%
japan4565
 
4.2%
mexico4493
 
4.2%
brazil4058
 
3.8%
russian3895
 
3.6%
federation3895
 
3.6%
philippines3160
 
2.9%
Other values (122)54825
50.9%
2026-01-26T10:06:18.870923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a95658
 
13.4%
i80550
 
11.3%
n72678
 
10.2%
e58230
 
8.1%
t35504
 
5.0%
r31054
 
4.3%
d30203
 
4.2%
o26432
 
3.7%
s25093
 
3.5%
l20016
 
2.8%
Other values (44)240576
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)715994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a95658
 
13.4%
i80550
 
11.3%
n72678
 
10.2%
e58230
 
8.1%
t35504
 
5.0%
r31054
 
4.3%
d30203
 
4.2%
o26432
 
3.7%
s25093
 
3.5%
l20016
 
2.8%
Other values (44)240576
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)715994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a95658
 
13.4%
i80550
 
11.3%
n72678
 
10.2%
e58230
 
8.1%
t35504
 
5.0%
r31054
 
4.3%
d30203
 
4.2%
o26432
 
3.7%
s25093
 
3.5%
l20016
 
2.8%
Other values (44)240576
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)715994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a95658
 
13.4%
i80550
 
11.3%
n72678
 
10.2%
e58230
 
8.1%
t35504
 
5.0%
r31054
 
4.3%
d30203
 
4.2%
o26432
 
3.7%
s25093
 
3.5%
l20016
 
2.8%
Other values (44)240576
33.6%

category
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.7 KiB
Sports
6936 
Animation
6600 
Action
6307 
Documentary
6188 
Drama
5929 
Other values (11)
56043 

Length

Max length11
Median length9
Mean length6.5330955
Min length3

Characters and Unicode

Total characters574932
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClassics
2nd rowClassics
3rd rowClassics
4th rowClassics
5th rowSports

Common Values

ValueCountFrequency (%)
Sports6936
 
7.9%
Animation6600
 
7.5%
Action6307
 
7.2%
Documentary6188
 
7.0%
Drama5929
 
6.7%
Sci-Fi5743
 
6.5%
Family5631
 
6.4%
Foreign5559
 
6.3%
Children5510
 
6.3%
New5199
 
5.9%
Other values (6)28401
32.3%

Length

2026-01-26T10:06:18.910297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sports6936
 
7.9%
animation6600
 
7.5%
action6307
 
7.2%
documentary6188
 
7.0%
drama5929
 
6.7%
sci-fi5743
 
6.5%
family5631
 
6.4%
foreign5559
 
6.3%
children5510
 
6.3%
new5199
 
5.9%
Other values (6)28401
32.3%

Most occurring characters

ValueCountFrequency (%)
i57284
 
10.0%
r49574
 
8.6%
o46108
 
8.0%
a44585
 
7.8%
n36764
 
6.4%
e36338
 
6.3%
m33562
 
5.8%
s31255
 
5.4%
c27829
 
4.8%
t26031
 
4.5%
Other values (20)185602
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)574932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i57284
 
10.0%
r49574
 
8.6%
o46108
 
8.0%
a44585
 
7.8%
n36764
 
6.4%
e36338
 
6.3%
m33562
 
5.8%
s31255
 
5.4%
c27829
 
4.8%
t26031
 
4.5%
Other values (20)185602
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)574932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i57284
 
10.0%
r49574
 
8.6%
o46108
 
8.0%
a44585
 
7.8%
n36764
 
6.4%
e36338
 
6.3%
m33562
 
5.8%
s31255
 
5.4%
c27829
 
4.8%
t26031
 
4.5%
Other values (20)185602
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)574932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i57284
 
10.0%
r49574
 
8.6%
o46108
 
8.0%
a44585
 
7.8%
n36764
 
6.4%
e36338
 
6.3%
m33562
 
5.8%
s31255
 
5.4%
c27829
 
4.8%
t26031
 
4.5%
Other values (20)185602
32.3%

Interactions

2026-01-26T10:06:13.967435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.435075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.867029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.351729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.815222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.331371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.765822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.269894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.727893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.154811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.620288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.056295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.485383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.998726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.471383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.899355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.385283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.849101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.363852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.800885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.304061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.760590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.185172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.652062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.088842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.519363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.031105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.504635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.933029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.421592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.884846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.397796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.835849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.338930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.792670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.217425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.685971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.121327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.551827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.065216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.539396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.011679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.457258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.920976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.432525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.872893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.376159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.827672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.251536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.721602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.156903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.637248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.098989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.572222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.046104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.493377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.956840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.466492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.908525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.411845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.863405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.283313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.755966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.190755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.670590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.130637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.604630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.080488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.529083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.991223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.497866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.943199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.447886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.896499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.366715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.788257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.224626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.703677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.166080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.639431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.116319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.566652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.077170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.535742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.977858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.484712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.930361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.400036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.824413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.259649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.739360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.200306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.674536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.151828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.603878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.118441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.570715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.014704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.519922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.964451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.433410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.858776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.295132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.774024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.232823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.707964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.184822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.639091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.153600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.604119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.048426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.554662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.994938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.464763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.891715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.326654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.805779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.263239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.739210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.217788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.672474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.192344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.635017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.082107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.587992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.026069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.494226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.922897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.357307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.837799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.295248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.770984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.251591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.708457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.227874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.668921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.116366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.622958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.057853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.525551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.957379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.390694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.869711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.327716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.803537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.285852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.744653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.263912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.701546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.200118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.657556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.091308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.557003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.990558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.421763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.902616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:14.359145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:08.834730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.319376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:09.780603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.298208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:10.734256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.236036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:11.693730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.123048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:12.588894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.023772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.454389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-26T10:06:13.933628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-26T10:06:18.942939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
activeactor_idaddress_idamountcategorycity_idcountry_idcustomer_idfilm_idinventory_idlengthpayment_idpostal_coderatingrental_durationrental_idrental_ratereplacement_coststaff_fnstaff_idstaff_lnstore_id
active1.0000.0000.0850.0270.0320.1090.0940.0920.0230.0210.0290.0920.1380.0140.0120.0210.0070.0180.0030.0030.0030.003
actor_id0.0001.0000.001-0.0070.0560.0020.0010.0010.0150.0150.0150.0010.0040.0430.041-0.0040.043-0.0010.0000.0000.0000.000
address_id0.0850.0011.0000.0140.0320.042-0.0421.000-0.003-0.003-0.0141.000-0.0430.0190.028-0.0030.027-0.0220.0760.0760.0760.076
amount0.027-0.0070.0141.0000.0650.0010.0130.0140.0270.0270.0030.014-0.0130.0340.1710.0020.705-0.0300.0160.0160.0160.016
category0.0320.0560.0320.0651.0000.0330.0290.0320.1390.1380.1490.0310.0290.1550.1610.0240.1450.1410.0300.0300.0300.030
city_id0.1090.0020.0420.0010.0331.000-0.0590.0420.0090.009-0.0020.042-0.0710.0200.021-0.0050.026-0.0010.1040.1040.1040.104
country_id0.0940.001-0.0420.0130.029-0.0591.000-0.0420.0170.0170.012-0.0420.0010.0240.021-0.0050.0200.0040.1020.1020.1020.102
customer_id0.0920.0011.0000.0140.0320.042-0.0421.000-0.003-0.003-0.0141.000-0.0430.0180.028-0.0030.027-0.0220.0740.0740.0740.074
film_id0.0230.015-0.0030.0270.1390.0090.017-0.0031.0001.0000.053-0.003-0.0100.1200.112-0.0010.112-0.0400.0340.0340.0340.034
inventory_id0.0210.015-0.0030.0270.1380.0090.017-0.0031.0001.0000.053-0.003-0.0100.1190.112-0.0010.116-0.0400.0330.0330.0330.033
length0.0290.015-0.0140.0030.149-0.0020.012-0.0140.0530.0531.000-0.014-0.0040.1010.0870.0010.1040.0140.0160.0160.0160.016
payment_id0.0920.0011.0000.0140.0310.042-0.0421.000-0.003-0.003-0.0141.000-0.0430.0170.028-0.0020.025-0.0220.0720.0720.0720.072
postal_code0.1380.004-0.043-0.0130.029-0.0710.001-0.043-0.010-0.010-0.004-0.0431.0000.0170.0150.0060.026-0.0010.0900.0900.0900.090
rating0.0140.0430.0190.0340.1550.0200.0240.0180.1200.1190.1010.0170.0171.0000.0960.0200.0380.1110.0150.0150.0150.015
rental_duration0.0120.0410.0280.1710.1610.0210.0210.0280.1120.1120.0870.0280.0150.0961.0000.0210.0720.0830.0070.0070.0070.007
rental_id0.021-0.004-0.0030.0020.024-0.005-0.005-0.003-0.001-0.0010.001-0.0020.0060.0200.0211.0000.0240.0040.0280.0280.0280.028
rental_rate0.0070.0430.0270.7050.1450.0260.0200.0270.1120.1160.1040.0250.0260.0380.0720.0241.0000.1090.0000.0000.0000.000
replacement_cost0.018-0.001-0.022-0.0300.141-0.0010.004-0.022-0.040-0.0400.014-0.022-0.0010.1110.0830.0040.1091.0000.0270.0270.0270.027
staff_fn0.0030.0000.0760.0160.0300.1040.1020.0740.0340.0330.0160.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000
staff_id0.0030.0000.0760.0160.0300.1040.1020.0740.0340.0330.0160.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000
staff_ln0.0030.0000.0760.0160.0300.1040.1020.0740.0340.0330.0160.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000
store_id0.0030.0000.0760.0160.0300.1040.1020.0740.0340.0330.0160.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000

Missing values

2026-01-26T10:06:14.454265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-26T10:06:14.639528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

film_idtitledescriptionrental_durationrental_ratelengthreplacement_costratingactor_idactor_fnactor_lncustomer_idcust_fncust_lnemailactiverental_idrental_datereturn_datepayment_idamountpayment_dateinventory_idstore_idstaff_idstaff_fnstaff_lnaddress_idaddresspostal_codecity_idcitycountry_idcountrycategory
0663PATIENT SISTERA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia70.999929.99NC-1790SEANGUINESS1MARYSMITHMARY.SMITH@sakilacustomer.org1762005-05-25 11:30:372005-06-03 12:00:3712.992005-05-25 11:30:37302111MikeHillyer51913 Hanoi Way35200463Sasebo50JapanClassics
1663PATIENT SISTERA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia70.999929.99NC-1774MILLAKEITEL1MARYSMITHMARY.SMITH@sakilacustomer.org1762005-05-25 11:30:372005-06-03 12:00:3712.992005-05-25 11:30:37302111MikeHillyer51913 Hanoi Way35200463Sasebo50JapanClassics
2663PATIENT SISTERA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia70.999929.99NC-1737VALBOLGER1MARYSMITHMARY.SMITH@sakilacustomer.org1762005-05-25 11:30:372005-06-03 12:00:3712.992005-05-25 11:30:37302111MikeHillyer51913 Hanoi Way35200463Sasebo50JapanClassics
3663PATIENT SISTERA Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia70.999929.99NC-1720LUCILLETRACY1MARYSMITHMARY.SMITH@sakilacustomer.org1762005-05-25 11:30:372005-06-03 12:00:3712.992005-05-25 11:30:37302111MikeHillyer51913 Hanoi Way35200463Sasebo50JapanClassics
4875TALENTED HOMICIDEA Lacklusture Panorama of a Dentist And a Forensic Psychologist who must Outrace a Pioneer in A U-Boat60.991739.99PG142JADARYDER1MARYSMITHMARY.SMITH@sakilacustomer.org15732005-05-28 10:35:232005-06-03 06:32:2320.992005-05-28 10:35:23402011MikeHillyer51913 Hanoi Way35200463Sasebo50JapanSports
5875TALENTED HOMICIDEA Lacklusture Panorama of a Dentist And a Forensic Psychologist who must Outrace a Pioneer in A U-Boat60.991739.99PG131JANEJACKMAN1MARYSMITHMARY.SMITH@sakilacustomer.org15732005-05-28 10:35:232005-06-03 06:32:2320.992005-05-28 10:35:23402011MikeHillyer51913 Hanoi Way35200463Sasebo50JapanSports
6875TALENTED HOMICIDEA Lacklusture Panorama of a Dentist And a Forensic Psychologist who must Outrace a Pioneer in A U-Boat60.991739.99PG85MINNIEZELLWEGER1MARYSMITHMARY.SMITH@sakilacustomer.org15732005-05-28 10:35:232005-06-03 06:32:2320.992005-05-28 10:35:23402011MikeHillyer51913 Hanoi Way35200463Sasebo50JapanSports
7875TALENTED HOMICIDEA Lacklusture Panorama of a Dentist And a Forensic Psychologist who must Outrace a Pioneer in A U-Boat60.991739.99PG44NICKSTALLONE1MARYSMITHMARY.SMITH@sakilacustomer.org15732005-05-28 10:35:232005-06-03 06:32:2320.992005-05-28 10:35:23402011MikeHillyer51913 Hanoi Way35200463Sasebo50JapanSports
8875TALENTED HOMICIDEA Lacklusture Panorama of a Dentist And a Forensic Psychologist who must Outrace a Pioneer in A U-Boat60.991739.99PG36BURTDUKAKIS1MARYSMITHMARY.SMITH@sakilacustomer.org15732005-05-28 10:35:232005-06-03 06:32:2320.992005-05-28 10:35:23402011MikeHillyer51913 Hanoi Way35200463Sasebo50JapanSports
9611MUSKETEERS WAITA Touching Yarn of a Student And a Moose who must Fight a Mad Cow in Australia74.997317.99PG152BENHARRIS1MARYSMITHMARY.SMITH@sakilacustomer.org111852005-06-15 00:54:122005-06-23 02:42:1235.992005-06-15 00:54:12278511MikeHillyer51913 Hanoi Way35200463Sasebo50JapanClassics
film_idtitledescriptionrental_durationrental_ratelengthreplacement_costratingactor_idactor_fnactor_lncustomer_idcust_fncust_lnemailactiverental_idrental_datereturn_datepayment_idamountpayment_dateinventory_idstore_idstaff_idstaff_fnstaff_lnaddress_idaddresspostal_codecity_idcitycountry_idcountrycategory
87993869SUSPECTS QUILLSA Emotional Epistle of a Pioneer And a Crocodile who must Battle a Man in A Manhattan Penthouse42.994722.99PG158VIVIENBASINGER599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157192005-08-23 11:08:462005-08-25 07:25:46160482.992005-08-23 11:08:46399022JonStephens6051325 Fukuyama Street27107537Tieli23ChinaAction
87994869SUSPECTS QUILLSA Emotional Epistle of a Pioneer And a Crocodile who must Battle a Man in A Manhattan Penthouse42.994722.99PG130GRETAKEITEL599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157192005-08-23 11:08:462005-08-25 07:25:46160482.992005-08-23 11:08:46399022JonStephens6051325 Fukuyama Street27107537Tieli23ChinaAction
87995869SUSPECTS QUILLSA Emotional Epistle of a Pioneer And a Crocodile who must Battle a Man in A Manhattan Penthouse42.994722.99PG112RUSSELLBACALL599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157192005-08-23 11:08:462005-08-25 07:25:46160482.992005-08-23 11:08:46399022JonStephens6051325 Fukuyama Street27107537Tieli23ChinaAction
87996869SUSPECTS QUILLSA Emotional Epistle of a Pioneer And a Crocodile who must Battle a Man in A Manhattan Penthouse42.994722.99PG85MINNIEZELLWEGER599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157192005-08-23 11:08:462005-08-25 07:25:46160482.992005-08-23 11:08:46399022JonStephens6051325 Fukuyama Street27107537Tieli23ChinaAction
87997869SUSPECTS QUILLSA Emotional Epistle of a Pioneer And a Crocodile who must Battle a Man in A Manhattan Penthouse42.994722.99PG70MICHELLEMCCONAUGHEY599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157192005-08-23 11:08:462005-08-25 07:25:46160482.992005-08-23 11:08:46399022JonStephens6051325 Fukuyama Street27107537Tieli23ChinaAction
87998869SUSPECTS QUILLSA Emotional Epistle of a Pioneer And a Crocodile who must Battle a Man in A Manhattan Penthouse42.994722.99PG40JOHNNYCAGE599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157192005-08-23 11:08:462005-08-25 07:25:46160482.992005-08-23 11:08:46399022JonStephens6051325 Fukuyama Street27107537Tieli23ChinaAction
8799983BLUES INSTINCTA Insightful Documentary of a Boat And a Composer who must Meet a Forensic Psychologist in An Abandoned Fun House52.995018.99G181MATTHEWCARREY599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157252005-08-23 11:25:002005-08-26 11:46:00160492.992005-08-23 11:25:0037822JonStephens6051325 Fukuyama Street27107537Tieli23ChinaFamily
8800083BLUES INSTINCTA Insightful Documentary of a Boat And a Composer who must Meet a Forensic Psychologist in An Abandoned Fun House52.995018.99G122SALMANOLTE599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157252005-08-23 11:25:002005-08-26 11:46:00160492.992005-08-23 11:25:0037822JonStephens6051325 Fukuyama Street27107537Tieli23ChinaFamily
8800183BLUES INSTINCTA Insightful Documentary of a Boat And a Composer who must Meet a Forensic Psychologist in An Abandoned Fun House52.995018.99G106GROUCHODUNST599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157252005-08-23 11:25:002005-08-26 11:46:00160492.992005-08-23 11:25:0037822JonStephens6051325 Fukuyama Street27107537Tieli23ChinaFamily
8800283BLUES INSTINCTA Insightful Documentary of a Boat And a Composer who must Meet a Forensic Psychologist in An Abandoned Fun House52.995018.99G24CAMERONSTREEP599AUSTINCINTRONAUSTIN.CINTRON@sakilacustomer.org1157252005-08-23 11:25:002005-08-26 11:46:00160492.992005-08-23 11:25:0037822JonStephens6051325 Fukuyama Street27107537Tieli23ChinaFamily